Dissimilarity measurement plays a crucial role in contentbased
image retrieval, where data objects and queries are represented as
vectors in high-dimensional content feature spaces. Given the large number
of dissimilarity measures that exist in many fields, a crucial research
question arises: Is there a dependency, if yes, what is the dependency,
of a dissimilarity measure's retrieval performance, on different feature
spaces? In this paper, we summarize fourteen core dissimilarity measures
and classify them into three categories. A systematic performance
comparison is carried out to test the effectiveness of these dissimilarity
measures with six different feature spaces and some of their combinations
on the Corel image collection. From our experimental results, we have
drawn a number of observations and insights on dissimilarity measurement
in content-based image retrieval, which will lay a foundation for
developing more effective image search technologies.